Application of SWIR hyperspectral imaging and chemometrics for identification of aflatoxin B1 contaminated maize kernels

Kimuli, Daniel; Wang, Wei; Jiang, Hongzhe; Zhao, Xin; Chu, Xuan

doi:10.1016/j.infrared.2018.01.026

Cited by 53 publications

(20 citation statements)

References 45 publications

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“…being TP the true positive and FN the false negatives. The best models are obtained when similar values are obtained for Sensitivity and Specificity in Cal and CV, thus demonstrating the robustness of the developed model (32). Cross validation is a very useful tool serving two critical functions in chemometric: (i) it enables an assessment of the optimal complexity of a model and (ii) it allows an estimation of the performance of a model when it is applied to unknown data.…”

Section: Instruments and Statistical Analysismentioning

confidence: 59%

“…Starting from the collected spectra it was thus possible to create a classification model able to recognize, inside a C&DW flow stream, the presence of uncontaminated (i.e., absence of asbestos fibers in the matrix) and contaminated samples (i.e., presence of asbestos fibers in the matrix). Multivariate approach, HIS based, was largely adopted in many research fields [29][30][31][32] to manage the huge amount of data and to utilize the information to identify, to characterize and to sort ACM. The aim of the chemometric approach was to obtain a data dimensionality reduction for a better data spectral evaluation and to develop classification algorithm for an efficient handling of multiple classes when hyperspectral imaging [33][34][35][36][37][38][39][40][41] sorting strategies have to be set up.…”

Section: Introductionmentioning

confidence: 99%

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Hyperspectral Imaging and Hierarchical PLS-DA Applied to Asbestos Recognition in Construction and Demolition Waste

2019

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Asbestos-Containing Materials (ACMs) are hazardous and prohibited to be sold or used as recycled materials. In the past, asbestos was widely used, together with cement, to produce “asbestos cement-based” products. During the recycling process of Construction and Demolition waste (C&DW), ACM must be collected and deposited separately from other wastes. One of the main aims of the recycling strategies applied to C&DW was thus to identify and separate ACM from C&DW (e.g., concrete and brick). However, to obtain a correct recovery of C&DW materials, control methodologies are necessary to evaluate the quality and the presence of harmful materials, such as ACM. HyperSpectral Imaging (HSI)-based sensing devices allow performing the full detection of materials constituting demolition waste. ACMs are, in fact, characterized by a spectral response that nakes them is different from the “simple” matrix of the material/s not embedding asbestos. The described HSI quality control approach is based on the utilization of a platform working in the short-wave infrared range (1000–2500 nm). The acquired hyperspectral images were analyzed by applying different chemometric methods: Principal Component Analysis for data exploration and hierarchical Partial Least-Square-Discriminant Analysis (PLS-DA) to build classification models. Following this approach, it was possible to set up a repeatable, reliable and efficient technique able to detect ACM presence inside a C&DW flow stream. Results showed that it is possible to discriminate and identify ACM inside C&DW. The recognition is potentially automatic, non-destructive and does not need any contact with the investigated products.

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Section: Instruments and Statistical Analysismentioning

confidence: 59%

Section: Introductionmentioning

confidence: 99%

Hyperspectral Imaging and Hierarchical PLS-DA Applied to Asbestos Recognition in Construction and Demolition Waste

2019

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“…In this way, the FDU-12@MIPs acted as an impressive adsorbent for the solid-phase extraction to enrich desired AFs in the real samples. Aflatoxin B1 contamination of maize kernels was detected by Kimuli et al, using short-wave infrared (SWIR) hyperspectral imaging (HSI) technique where the maize kernels were categorized by some analytical approaches, including principal component analysis (PCA), partial least squares discriminant analysis (PLSDA) and factorial discriminant analysis (FDA) [140]. Based on the PCA findings, the control kernels were partially separated from kernels contaminated by AFB1 for each variety, but there was no pattern of separation between the pooled samples.…”

Section: Af Detection Strategiesmentioning

confidence: 99%

Characteristics, Occurrence, Detection and Detoxification of Aflatoxins in Foods and Feeds

Nazhand

Durazzo²,

Lucarini³

et al. 2020

Foods

106

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Mycotoxin contamination continues to be a food safety concern globally, with the most toxic being aflatoxins. On-farm aflatoxins, during food transit or storage, directly or indirectly result in the contamination of foods, which affects the liver, immune system and reproduction after infiltration into human beings and animals. There are numerous reports on aflatoxins focusing on achieving appropriate methods for quantification, precise detection and control in order to ensure consumer safety. In 2012, the International Agency for Research on Cancer (IARC) classified aflatoxins B1, B2, G1, G2, M1 and M2 as group 1 carcinogenic substances, which are a global human health concern. Consequently, this review article addresses aflatoxin chemical properties and biosynthetic processes; aflatoxin contamination in foods and feeds; health effects in human beings and animals due to aflatoxin exposure, as well as aflatoxin detection and detoxification methods.

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“…Kimuli et al 60 also used SWIR hyperspectral imaging system (1000 -2500 nm) to detect aflatoxin B1 combined with PCA, PLS-DA and FDA techniques to explore and classify maize kernels of four varieties from different States of the USA. Cortés et al 61 discriminated two nectarine varieties in different spectral ranges of NIR and Vis-NIR using PLS-DA and LDA models.…”

Section: Food Analysismentioning

confidence: 99%

Chemometrics in analytical chemistry – an overview of applications from 2014 to 2018

et al. 2019

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A compilation of papers published between 2014 and 2018 was evaluated. Many papers related to multivariate calibration and classification have been reported, as well as, design of experiments applications and artificial intelligence methods. Some applications were highlighted, as medical and pharmaceutical, food analysis, fuels, biological and forensic for the chemometric techniques on this review. Most studies are related to developing methods for practical solutions in industry or routine analysis. A promising scenario is shown considering the number of published papers: a total of 832 for this period using the keywords, multivariate classification, multivariate calibration, analysis, chemometrics, prediction, analytical chemistry, artificial neural networks (ANN), design of experiments (DoE) and factorial design. An useful overview for Analytical Chemistry researchers´ combined with Chemometrics is presented in this review.

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Application of SWIR hyperspectral imaging and chemometrics for identification of aflatoxin B1 contaminated maize kernels

Cited by 53 publications

References 45 publications

Hyperspectral Imaging and Hierarchical PLS-DA Applied to Asbestos Recognition in Construction and Demolition Waste

Hyperspectral Imaging and Hierarchical PLS-DA Applied to Asbestos Recognition in Construction and Demolition Waste

Characteristics, Occurrence, Detection and Detoxification of Aflatoxins in Foods and Feeds

Chemometrics in analytical chemistry – an overview of applications from 2014 to 2018

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